{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPU Open Analytics Initiative: connect MapD to PyGDF to H2OAIGLM\n",
    "\n",
    "### In this demo, we will train 4000 regularized linear regression models on the U.S. Census dataset, with the goal to predict the income of a person, given approximately 10000 data points (such as age, occupation, zip code, etc.)\n",
    "\n",
    "### The dataset is about 2GB in memory (50k rows, 10k cols, single-precision floating-point values), so it fits onto the GPU memory.\n",
    "\n",
    "### By using multiple GPUs, we are able to speed up this process significantly, and can train about 40 models per second (on a DGX-1 with 8 GPUs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose to Run or Run+Animate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# run=0: Choose not run h2oaiglm and just pass through all other cells\n",
    "# run=1: Run h2oaiglm without animation\n",
    "# Requirements: python3.5\n",
    "run=1\n",
    "\n",
    "# anim=0: Don't show animation\n",
    "# anim=1: Do show animation\n",
    "# pip install pandas psutil matplotlib --user\n",
    "# pip install -e git+https://github.com/fbcotter/py3nvml#egg=py3nvml --user\n",
    "anim=1\n",
    "\n",
    "PWD = !pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os.path\n",
    "from pprint import pprint\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add import path to MapD Thrift binding and Arrow schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/vinod/Downloads/pygdf/notebooks/../thirdparty'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thirdparty_path = os.path.join(PWD[0], '..', 'thirdparty')\n",
    "sys.path.append(thirdparty_path)\n",
    "thirdparty_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If `pygdf` cannot be imported, uncomment code below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pygdf_path = os.path.join(PWD[0], '..')\n",
    "sys.path.append(pygdf_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pygdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from thrift.protocol import TBinaryProtocol\n",
    "from thrift.protocol import TJSONProtocol\n",
    "from thrift.transport import TSocket\n",
    "from thrift.transport import THttpClient\n",
    "from thrift.transport import TTransport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mapd import MapD\n",
    "from mapd import ttypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MapD connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_client(host_or_uri, port, http):\n",
    "  if http:\n",
    "    transport = THttpClient.THttpClient(host_or_uri)\n",
    "    protocol = TJSONProtocol.TJSONProtocol(transport)\n",
    "  else:\n",
    "    socket = TSocket.TSocket(host_or_uri, port)\n",
    "    transport = TTransport.TBufferedTransport(socket)\n",
    "    protocol = TBinaryProtocol.TBinaryProtocol(transport)\n",
    "\n",
    "  client = MapD.Client(protocol)\n",
    "  transport.open()\n",
    "  return client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection complete\n"
     ]
    }
   ],
   "source": [
    "db_name = 'mapd'\n",
    "user_name = 'mapd'\n",
    "passwd = 'HyperInteractive'\n",
    "hostname = 'localhost'\n",
    "portno = 9091\n",
    "\n",
    "client = get_client(hostname, portno, False)\n",
    "session = client.connect(user_name, passwd, db_name)\n",
    "print('Connection complete')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "447\n"
     ]
    }
   ],
   "source": [
    "columns = \"\"\"\n",
    "INCEARN,RECTYPE,IPUMS_YEAR,DATANUM,SERIAL,NUMPREC,SUBSAMP,HHWT,HHTYPE,REPWT,ADJUST,CPI99,REGION,STATEICP,STATEFIP,COUNTY,COUNTYFIPS,METRO,METAREA,METAREAD,MET2013,MET2013ERR,CITY,CITYERR,CITYPOP,PUMA,PUMARES2MIG,STRATA,PUMASUPR,CONSPUMA,CPUMA0010,APPAL,APPALD,HOMELAND,MET2003,CNTRY,GQ,GQTYPE,GQTYPED,FARM,OWNERSHP,OWNERSHPD,MORTGAGE,MORTGAG2,COMMUSE,FARMPROD,ACREHOUS,MORTAMT1,MORTAMT2,TAXINCL,INSINCL,PROPINSR,PROPTX99,OWNCOST,RENT,RENTGRS,RENTMEAL,CONDOFEE,MOBLHOME,MOBLHOM2,MOBLOAN,SECRES,SECRESMO,SECRESRE,COSTELEC,COSTGAS,COSTWATR,COSTFUEL,PUBHOUS,RENTSUB,HEATSUB,LUNCHSUB,FOODSTMP,FDSTPAMT,VALUEH,LINGISOL,VACANCY,KITCHEN,KITCHENORIG,FRIDGE,FRIDGEORIG,SINK,STOVE,ROOMS,ROOMSORIG,PLUMBING,HOTWATER,SHOWER,TOILET,BUILTYR,BUILTYR2,UNITSSTR,BEDROOMS,BEDROOMSORIG,PHONE,PHONEORIG,CILAPTOP,CIHAND,CIOTHCOMP,CINETHH,CIMODEM,CISAT,CIDSL,CIFIBER,CIBRDBND,CIDIAL,CIOTHSVC,FUELHEAT,VEHICLES,SSMC,NFAMS,NSUBFAM,NCOUPLES,NMOTHERS,NFATHERS,MULTGEN,MULTGEND,CBNSUBFAM,REPWT1,REPWT2,REPWT3,REPWT4,REPWT5,REPWT6,REPWT7,REPWT8,REPWT9,REPWT10,REPWT11,REPWT12,REPWT13,REPWT14,REPWT15,REPWT16,REPWT17,REPWT18,REPWT19,REPWT20,REPWT21,REPWT22,REPWT23,REPWT24,REPWT25,REPWT26,REPWT27,REPWT28,REPWT29,REPWT30,REPWT31,REPWT32,REPWT33,REPWT34,REPWT35,REPWT36,REPWT37,REPWT38,REPWT39,REPWT40,REPWT41,REPWT42,REPWT43,REPWT44,REPWT45,REPWT46,REPWT47,REPWT48,REPWT49,REPWT50,REPWT51,REPWT52,REPWT53,REPWT54,REPWT55,REPWT56,REPWT57,REPWT58,REPWT59,REPWT60,REPWT61,REPWT62,REPWT63,REPWT64,REPWT65,REPWT66,REPWT67,REPWT68,REPWT69,REPWT70,REPWT71,REPWT72,REPWT73,REPWT74,REPWT75,REPWT76,REPWT77,REPWT78,REPWT79,REPWT80,RESPMODE,PERNUM,PERWT,SLWT,REPWTP,FAMSIZE,NCHILD,NCHLT5,FAMUNIT,ELDCH,YNGCH,NSIBS,MOMLOC,STEPMOM,MOMRULE,POPLOC,STEPPOP,POPRULE,SPLOC,SPRULE,SUBFAM,SFTYPE,SFRELATE,CBSUBFAM,CBSFTYPE,CBSFRELATE,RELATE,RELATED,SEX,AGE,AGEORIG,BIRTHQTR,MARST,BIRTHYR,MARRNO,MARRINYR,YRMARR,DIVINYR,WIDINYR,FERTYR,RACE,RACED,HISPAN,HISPAND,BPL,BPLD,ANCESTR1,ANCESTR1D,ANCESTR2,ANCESTR2D,CITIZEN,YRNATUR,YRIMMIG,YRSUSA1,YRSUSA2,SPOKEN_LANGUAGE,LANGUAGED,SPEAKENG,TRIBE,TRIBED,RACESING,RACESINGD,RACAMIND,RACASIAN,RACBLK,RACPACIS,RACWHT,RACOTHER,RACNUM,SCHOOL,EDUC,EDUCD,GRADEATT,GRADEATTD,SCHLTYPE,DEGFIELD,DEGFIELDD,DEGFIELD2,DEGFIELD2D,EMPSTAT,EMPSTATD,LABFORCE,OCC,OCC1950,OCC1990,OCC2010,IND,IND1950,IND1990,CLASSWKR,CLASSWKRD,OCCSOC,INDNAICS,WKSWORK1,WKSWORK2,UHRSWORK,WRKLSTWK,ABSENT,LOOKING,AVAILBLE,WRKRECAL,WORKEDYR,POVERTY,OCCSCORE,SEI,HWSEI,PRESGL,PRENT,ERSCOR50,ERSCOR90,EDSCOR50,EDSCOR90,NPBOSS50,NPBOSS90,MIGRATE1,MIGRATE1D,MIGPLAC1,MIGMET1,MIGTYPE1,MIGCITY1,MIGPUMS1,MIGPUMA1,MOVEDIN,MOVEDINORIG,DISABWRK,VETDISAB,DIFFREM,DIFFPHYS,DIFFMOB,DIFFCARE,DIFFSENS,DIFFEYE,DIFFHEAR,VETSTAT,VETSTATD,VET01LTR,VET95X00,VET90X01,VET90X95,VET75X90,VET80X90,VET75X80,VETVIETN,VET55X64,VETKOREA,VET47X50,VETWWII,VETOTHER,VETYRS,PWSTATE2,PWMETRO,PWCITY,PWTYPE,PWPUMA00,PWPUMAS,TRANWORK,CARPOOL,RIDERS,TRANTIME,DEPARTS,ARRIVES,GCHOUSE,GCMONTHS,GCRESPON,PROBAI,PROBAPI,PROBBLK,PROBOTH,PROBWHT,REPWTP1,REPWTP2,REPWTP3,REPWTP4,REPWTP5,REPWTP6,REPWTP7,REPWTP8,REPWTP9,REPWTP10,REPWTP11,REPWTP12,REPWTP13,REPWTP14,REPWTP15,REPWTP16,REPWTP17,REPWTP18,REPWTP19,REPWTP20,REPWTP21,REPWTP22,REPWTP23,REPWTP24,REPWTP25,REPWTP26,REPWTP27,REPWTP28,REPWTP29,REPWTP30,REPWTP31,REPWTP32,REPWTP33,REPWTP34,REPWTP35,REPWTP36,REPWTP37,REPWTP38,REPWTP39,REPWTP40,REPWTP41,REPWTP42,REPWTP43,REPWTP44,REPWTP45,REPWTP46,REPWTP47,REPWTP48,REPWTP49,REPWTP50,REPWTP51,REPWTP52,REPWTP53,REPWTP54,REPWTP55,REPWTP56,REPWTP57,REPWTP58,REPWTP59,REPWTP60,REPWTP61,REPWTP62,REPWTP63,REPWTP64,REPWTP65,REPWTP66,REPWTP67,REPWTP68,REPWTP69,REPWTP70,REPWTP71,REPWTP72,REPWTP73,REPWTP74,REPWTP75,REPWTP76,REPWTP77,REPWTP78,REPWTP79,REPWTP80\n",
    "\"\"\".strip()\n",
    "print(len(columns.split(',')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query is : SELECT INCEARN,RECTYPE,IPUMS_YEAR,DATANUM,SERIAL,NUMPREC,SUBSAMP,HHWT,HHTYPE,REPWT,ADJUST,CPI99,REGION,STATEICP,STATEFIP,COUNTY,COUNTYFIPS,METRO,METAREA,METAREAD,MET2013,MET2013ERR,CITY,CITYERR,CITYPOP,PUMA,PUMARES2MIG,STRATA,PUMASUPR,CONSPUMA,CPUMA0010,APPAL,APPALD,HOMELAND,MET2003,CNTRY,GQ,GQTYPE,GQTYPED,FARM,OWNERSHP,OWNERSHPD,MORTGAGE,MORTGAG2,COMMUSE,FARMPROD,ACREHOUS,MORTAMT1,MORTAMT2,TAXINCL,INSINCL,PROPINSR,PROPTX99,OWNCOST,RENT,RENTGRS,RENTMEAL,CONDOFEE,MOBLHOME,MOBLHOM2,MOBLOAN,SECRES,SECRESMO,SECRESRE,COSTELEC,COSTGAS,COSTWATR,COSTFUEL,PUBHOUS,RENTSUB,HEATSUB,LUNCHSUB,FOODSTMP,FDSTPAMT,VALUEH,LINGISOL,VACANCY,KITCHEN,KITCHENORIG,FRIDGE,FRIDGEORIG,SINK,STOVE,ROOMS,ROOMSORIG,PLUMBING,HOTWATER,SHOWER,TOILET,BUILTYR,BUILTYR2,UNITSSTR,BEDROOMS,BEDROOMSORIG,PHONE,PHONEORIG,CILAPTOP,CIHAND,CIOTHCOMP,CINETHH,CIMODEM,CISAT,CIDSL,CIFIBER,CIBRDBND,CIDIAL,CIOTHSVC,FUELHEAT,VEHICLES,SSMC,NFAMS,NSUBFAM,NCOUPLES,NMOTHERS,NFATHERS,MULTGEN,MULTGEND,CBNSUBFAM,REPWT1,REPWT2,REPWT3,REPWT4,REPWT5,REPWT6,REPWT7,REPWT8,REPWT9,REPWT10,REPWT11,REPWT12,REPWT13,REPWT14,REPWT15,REPWT16,REPWT17,REPWT18,REPWT19,REPWT20,REPWT21,REPWT22,REPWT23,REPWT24,REPWT25,REPWT26,REPWT27,REPWT28,REPWT29,REPWT30,REPWT31,REPWT32,REPWT33,REPWT34,REPWT35,REPWT36,REPWT37,REPWT38,REPWT39,REPWT40,REPWT41,REPWT42,REPWT43,REPWT44,REPWT45,REPWT46,REPWT47,REPWT48,REPWT49,REPWT50,REPWT51,REPWT52,REPWT53,REPWT54,REPWT55,REPWT56,REPWT57,REPWT58,REPWT59,REPWT60,REPWT61,REPWT62,REPWT63,REPWT64,REPWT65,REPWT66,REPWT67,REPWT68,REPWT69,REPWT70,REPWT71,REPWT72,REPWT73,REPWT74,REPWT75,REPWT76,REPWT77,REPWT78,REPWT79,REPWT80,RESPMODE,PERNUM,PERWT,SLWT,REPWTP,FAMSIZE,NCHILD,NCHLT5,FAMUNIT,ELDCH,YNGCH,NSIBS,MOMLOC,STEPMOM,MOMRULE,POPLOC,STEPPOP,POPRULE,SPLOC,SPRULE,SUBFAM,SFTYPE,SFRELATE,CBSUBFAM,CBSFTYPE,CBSFRELATE,RELATE,RELATED,SEX,AGE,AGEORIG,BIRTHQTR,MARST,BIRTHYR,MARRNO,MARRINYR,YRMARR,DIVINYR,WIDINYR,FERTYR,RACE,RACED,HISPAN,HISPAND,BPL,BPLD,ANCESTR1,ANCESTR1D,ANCESTR2,ANCESTR2D,CITIZEN,YRNATUR,YRIMMIG,YRSUSA1,YRSUSA2,SPOKEN_LANGUAGE,LANGUAGED,SPEAKENG,TRIBE,TRIBED,RACESING,RACESINGD,RACAMIND,RACASIAN,RACBLK,RACPACIS,RACWHT,RACOTHER,RACNUM,SCHOOL,EDUC,EDUCD,GRADEATT,GRADEATTD,SCHLTYPE,DEGFIELD,DEGFIELDD,DEGFIELD2,DEGFIELD2D,EMPSTAT,EMPSTATD,LABFORCE,OCC,OCC1950,OCC1990,OCC2010,IND,IND1950,IND1990,CLASSWKR,CLASSWKRD,OCCSOC,INDNAICS,WKSWORK1,WKSWORK2,UHRSWORK,WRKLSTWK,ABSENT,LOOKING,AVAILBLE,WRKRECAL,WORKEDYR,POVERTY,OCCSCORE,SEI,HWSEI,PRESGL,PRENT,ERSCOR50,ERSCOR90,EDSCOR50,EDSCOR90,NPBOSS50,NPBOSS90,MIGRATE1,MIGRATE1D,MIGPLAC1,MIGMET1,MIGTYPE1,MIGCITY1,MIGPUMS1,MIGPUMA1,MOVEDIN,MOVEDINORIG,DISABWRK,VETDISAB,DIFFREM,DIFFPHYS,DIFFMOB,DIFFCARE,DIFFSENS,DIFFEYE,DIFFHEAR,VETSTAT,VETSTATD,VET01LTR,VET95X00,VET90X01,VET90X95,VET75X90,VET80X90,VET75X80,VETVIETN,VET55X64,VETKOREA,VET47X50,VETWWII,VETOTHER,VETYRS,PWSTATE2,PWMETRO,PWCITY,PWTYPE,PWPUMA00,PWPUMAS,TRANWORK,CARPOOL,RIDERS,TRANTIME,DEPARTS,ARRIVES,GCHOUSE,GCMONTHS,GCRESPON,PROBAI,PROBAPI,PROBBLK,PROBOTH,PROBWHT,REPWTP1,REPWTP2,REPWTP3,REPWTP4,REPWTP5,REPWTP6,REPWTP7,REPWTP8,REPWTP9,REPWTP10,REPWTP11,REPWTP12,REPWTP13,REPWTP14,REPWTP15,REPWTP16,REPWTP17,REPWTP18,REPWTP19,REPWTP20,REPWTP21,REPWTP22,REPWTP23,REPWTP24,REPWTP25,REPWTP26,REPWTP27,REPWTP28,REPWTP29,REPWTP30,REPWTP31,REPWTP32,REPWTP33,REPWTP34,REPWTP35,REPWTP36,REPWTP37,REPWTP38,REPWTP39,REPWTP40,REPWTP41,REPWTP42,REPWTP43,REPWTP44,REPWTP45,REPWTP46,REPWTP47,REPWTP48,REPWTP49,REPWTP50,REPWTP51,REPWTP52,REPWTP53,REPWTP54,REPWTP55,REPWTP56,REPWTP57,REPWTP58,REPWTP59,REPWTP60,REPWTP61,REPWTP62,REPWTP63,REPWTP64,REPWTP65,REPWTP66,REPWTP67,REPWTP68,REPWTP69,REPWTP70,REPWTP71,REPWTP72,REPWTP73,REPWTP74,REPWTP75,REPWTP76,REPWTP77,REPWTP78,REPWTP79,REPWTP80 FROM ipums_easy WHERE INCEARN > 100 order by SERIAL;\n"
     ]
    }
   ],
   "source": [
    "#query = \"SELECT {} FROM ipums_easy WHERE INCEARN > 100;\".format(columns)\n",
    "# ensure sql query is deterministic\n",
    "query = \"SELECT {} FROM ipums_easy WHERE INCEARN > 100 order by SERIAL;\".format(columns)\n",
    "print('Query is : ' + query)\n",
    "\n",
    "# always use True for is columnar\n",
    "results = client.sql_execute_cudf(session, query, device_id=0, first_n=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numba import cuda\n",
    "from numba.cuda.cudadrv import drvapi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<numba.cuda.cudadrv.driver.OwnedPointer at 0x7f8253707ac8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipc_handle = drvapi.cu_ipc_mem_handle(*results.df_handle)\n",
    "ipch = cuda.driver.IpcHandle(None, ipc_handle, size=results.df_size)\n",
    "ctx = cuda.current_context()\n",
    "dptr = ipch.open(ctx)\n",
    "\n",
    "dptr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`dptr` is GPU memory containing the query result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert `dptr` into a GPU device ndarray (numpy array like object on GPU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtype = np.dtype(np.byte)\n",
    "darr = cuda.devicearray.DeviceNDArray(shape=dptr.size, strides=dtype.itemsize, dtype=dtype, gpu_data=dptr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use PyGDF to read the arrow metadata from the query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pygdf.gpuarrow import GpuArrowReader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reader = GpuArrowReader(darr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wrap result in a Python CUDA DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pygdf.dataframe import DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = DataFrame(reader.to_dict().items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32876.621408045976"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['INCEARN'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocess the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_cols = set()\n",
    "cat_cols = set()\n",
    "response_set = set(['INCEARN'])\n",
    "feature_names = set(df.columns) - response_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine categorical and numeric columns.  Compute unique values from categorical columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "uniques = {}\n",
    "for k in feature_names:\n",
    "    try:\n",
    "        uniquevals = df[k].unique_k(k=1000)\n",
    "        uniques[k] = uniquevals\n",
    "    except ValueError:\n",
    "        # more than 1000 unique values\n",
    "        num_cols.add(k)\n",
    "    else:\n",
    "        # within 1000 unique values\n",
    "        nunique = len(uniquevals)\n",
    "        if nunique < 2:\n",
    "            del df[k]        # drop constant column\n",
    "        elif 1 < nunique < 1000:\n",
    "            cat_cols.add(k)  # as cat column\n",
    "        else:\n",
    "            num_cols.add(k)  # as num column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fix numeric columns.  Fill NA, Scale to [0, 1]. Drop near constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for k in (num_cols - response_set):\n",
    "    df[k] = df[k].fillna(df[k].mean())\n",
    "    assert df[k].null_count == 0\n",
    "    std = df[k].std()\n",
    "    # drop near constant columns\n",
    "    if not np.isfinite(std) or std < 1e-4:\n",
    "        del df[k]\n",
    "        print('drop near constant', k)\n",
    "    else:\n",
    "        df[k] = df[k].scale()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expand categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for k in cat_cols:\n",
    "    cats = uniques[k][1:]  # drop first\n",
    "    df = df.one_hot_encoding(k, prefix=k, cats=cats)\n",
    "    del df[k]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensure INCEARN is float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5568\n"
     ]
    }
   ],
   "source": [
    "nrows=len(df)\n",
    "print(nrows)\n",
    "df['intercept'] = np.ones(nrows, dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['INCEARN'] = df['INCEARN'].astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{dtype('float64')}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print dtypes\n",
    "{df[k].dtype for k in df.columns}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create 60-40: training - testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60% of 5568 is 4454\n",
      "train_df has 4454 rows | test_df has 1114 rows\n"
     ]
    }
   ],
   "source": [
    "# Fraction for train (test is 1-FRACTION)\n",
    "FRACTION=0.8\n",
    "validFraction=1.0-FRACTION\n",
    "n60 = int(len(df) * FRACTION)\n",
    "print('60% of {} is {}'.format(len(df), n60))\n",
    "train_df = df.loc[:n60]\n",
    "if FRACTION<1.0:\n",
    "    test_df = df.loc[n60:]\n",
    "    print('train_df has {} rows | test_df has {} rows'.format(len(train_df), len(test_df)))\n",
    "else:\n",
    "    print('train_df has {} rows | test_df has {} rows'.format(len(train_df), 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turn the dataframes into a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data_mat = train_df.as_gpu_matrix(columns=df.columns[1:])\n",
    "train_result_mat = train_df.as_gpu_matrix(columns=[df.columns[0]])\n",
    "if FRACTION<1.0:\n",
    "    test_data_mat = test_df.as_gpu_matrix(columns=df.columns[1:])\n",
    "    test_result_mat = test_df.as_gpu_matrix(columns=[df.columns[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#print(train_df.as_matrix(columns=[df.columns[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33154.5410867\n",
      "31765.4416517\n"
     ]
    }
   ],
   "source": [
    "print(train_df['INCEARN'].mean())\n",
    "if FRACTION<1.0:\n",
    "    print(test_df['INCEARN'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4454, 8550)\n",
      "(4454, 1)\n",
      "(1114, 8550)\n",
      "(1114, 1)\n"
     ]
    }
   ],
   "source": [
    "print(train_data_mat.shape)\n",
    "print(train_result_mat.shape)\n",
    "if FRACTION<1.0:\n",
    "    print(test_data_mat.shape)\n",
    "    print(test_result_mat.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleanup the IPC handle.\n",
    "\n",
    "Note: `.as_gpu_matrix()` has created new copies; thus, we can close the IPC handle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ipch.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ctypes pointer to the gpu matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_data_mat_ptr address 0x10323200000\n",
      "train_result_mat_ptr address 0x1032300ae00\n",
      "test_data_mat_ptr address 0x10335600000\n",
      "test_result_mat_ptr address 0x1033a000000\n"
     ]
    }
   ],
   "source": [
    "train_data_mat_ptr = train_data_mat.device_ctypes_pointer\n",
    "train_result_mat_ptr = train_result_mat.device_ctypes_pointer\n",
    "print('train_data_mat_ptr address', hex(train_data_mat_ptr.value))\n",
    "print('train_result_mat_ptr address', hex(train_result_mat_ptr.value))\n",
    "if FRACTION<1.0:\n",
    "    test_data_mat_ptr = test_data_mat.device_ctypes_pointer\n",
    "    test_result_mat_ptr = test_result_mat.device_ctypes_pointer\n",
    "    print('test_data_mat_ptr address', hex(test_data_mat_ptr.value))\n",
    "    print('test_result_mat_ptr address', hex(test_result_mat_ptr.value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/vinod/Downloads/pygdf/notebooks'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import H2OAIGLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loaded H2OAIGLM CPU library\n",
      "\n",
      "Loaded H2OAIGLM GPU library.\n"
     ]
    }
   ],
   "source": [
    "# Load H2OAIGLM\n",
    "import h2oaiglm as h2oaiglm\n",
    "from ctypes import *\n",
    "import time\n",
    "if anim==1:\n",
    "    import pandas as pd\n",
    "\n",
    "a=c_void_p(train_data_mat_ptr.value)\n",
    "b=c_void_p(train_result_mat_ptr.value)\n",
    "if FRACTION<1.0:\n",
    "    c=c_void_p(test_data_mat_ptr.value)\n",
    "    d=c_void_p(test_result_mat_ptr.value)\n",
    "else:\n",
    "    c=c_void_p(0)\n",
    "    d=c_void_p(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define some helper methods for plotting and running the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Number of GPUS: 2\n"
     ]
    }
   ],
   "source": [
    "def new_alpha(row_fold):\n",
    "    if row_fold == 0:\n",
    "        return -0.025\n",
    "    elif row_fold == 1:\n",
    "        return -0.05\n",
    "    elif row_fold == 3:\n",
    "        return 0.025\n",
    "    elif row_fold == 4:\n",
    "        return 0.05\n",
    "    else: return 0\n",
    "\n",
    "def plot_cpu_perf(axis, cpu_labels, cpu_snapshot):\n",
    "    axis.cla()\n",
    "    axis.grid(False)\n",
    "    axis.set_ylim([0,100])\n",
    "    axis.set_ylabel('Percent', labelpad=2, fontsize = 14)\n",
    "    axis.bar(cpu_labels, cpu_snapshot, color='dodgerblue')\n",
    "    axis.set_title('CPU Utilization', fontsize = 16)\n",
    "    \n",
    "def plot_gpu_perf(axis, gpu_labels, gpu_snapshot):\n",
    "    axis.cla()\n",
    "    axis.grid(False)\n",
    "    axis.set_ylim([0,100])\n",
    "    axis.set_xticks(gpu_labels)\n",
    "    axis.set_ylabel('Percent', labelpad=2, fontsize = 14)\n",
    "    axis.bar(gpu_labels, gpu_snapshot, width =0.5, color = 'limegreen')\n",
    "    axis.set_title('GPU Utilization', fontsize = 16)\n",
    "    \n",
    "def plot_glm_results(axis, results, best_rmse, cb):\n",
    "    axis.cla()\n",
    "    axis.set_xscale('log')\n",
    "    axis.set_xlim([0.1, 1e9])\n",
    "    axis.set_ylim([-0.12, 1.12])\n",
    "    axis.set_yticks([x/7. for x in range(0,8)])\n",
    "    axis.set_ylabel('Parameter 1:  '+r'$\\alpha$', fontsize = 16)\n",
    "    axis.set_xlabel('Parameter 2:  '+r'$\\lambda$', fontsize = 16)\n",
    "    num_models = min(4000,int(4000*results.shape[0]/2570))\n",
    "    axis.set_title('Elastic Net Models Trained and Evaluated: ' + str(num_models), fontsize = 16)\n",
    "\n",
    "    try:\n",
    "        cm = ListedColormap(sns.color_palette(\"RdYlGn\", 10).as_hex())\n",
    "        cf = axis.scatter(results['lambda'], results['alpha_prime'], c=results['rel_acc'], \n",
    "                    cmap=cm, vmin=0, vmax=1)\n",
    "        axis.plot(best_rmse['lambda'],best_rmse['alpha_prime'], 'o',\n",
    "            ms=15, mec='k', mfc='none', mew=2)\n",
    "\n",
    "        if not cb:\n",
    "            cb = pl.colorbar(cf, ax=axis)\n",
    "            cb.set_label('Relative  Validation  Accuracy', rotation=270, \n",
    "                         labelpad=18, fontsize = 16)   \n",
    "        cb.update_normal(cf)\n",
    "    except:\n",
    "        #print(\"plot_glm_results exception -- no frame\")\n",
    "        pass\n",
    "\n",
    "from py3nvml.py3nvml import *\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "import psutil\n",
    "import numpy as np\n",
    "import pylab as pl\n",
    "from IPython import display\n",
    "import matplotlib.gridspec as gridspec\n",
    "from matplotlib.colors import ListedColormap\n",
    "import sys\n",
    "import subprocess\n",
    "maxNGPUS = int(subprocess.check_output(\"nvidia-smi -L | wc -l\", shell=True))\n",
    "print(\"Maximum Number of GPUS:\", maxNGPUS)\n",
    "\n",
    "nvmlInit()\n",
    "deviceCount = nvmlDeviceGetCount()\n",
    "for i in range(deviceCount):\n",
    "    handle = nvmlDeviceGetHandleByIndex(i)\n",
    "    #print(\"Device {}: {}\".format(i, nvmlDeviceGetName(handle)))\n",
    "    #print (\"Driver Version:\", nvmlSystemGetDriverVersion())\n",
    "\n",
    "import os\n",
    "def RunAnimation(arg):\n",
    "    deviceCount = arg\n",
    "    file = os.getcwd() + \"/rmse.txt\"\n",
    "    fig = pl.figure(figsize = (9,9))\n",
    "    pl.rcParams['xtick.labelsize'] = 14\n",
    "    pl.rcParams['ytick.labelsize'] = 14\n",
    "    gs = gridspec.GridSpec(3, 2, wspace=0.3, hspace=0.4)\n",
    "    ax1 = pl.subplot(gs[0,-2])\n",
    "    ax2 = pl.subplot(gs[0,1])\n",
    "    ax3 = pl.subplot(gs[1:,:])\n",
    "    fig.suptitle('H2O.ai Machine Learning $-$ Generalized Linear Modeling', size=18)\n",
    "\n",
    "    pl.gcf().subplots_adjust(bottom=0.2)\n",
    "\n",
    "    cb = False\n",
    "    os.system(\"mkdir -p images\")\n",
    "    i=0\n",
    "    while(True):\n",
    "        try:\n",
    "            #cpu\n",
    "            snapshot = psutil.cpu_percent(percpu=True)\n",
    "            cpu_labels = range(1,len(snapshot)+1)\n",
    "            plot_cpu_perf(ax1, cpu_labels, snapshot)\n",
    "    \n",
    "            #gpu\n",
    "            gpu_snapshot = []\n",
    "            gpu_labels = list(range(1,deviceCount+1))\n",
    "            for j in range(deviceCount):\n",
    "                handle = nvmlDeviceGetHandleByIndex(j)\n",
    "                util = nvmlDeviceGetUtilizationRates(handle)\n",
    "                gpu_snapshot.append(util.gpu)\n",
    "            gpu_snapshot = gpu_snapshot   \n",
    "            plot_gpu_perf(ax2, gpu_labels, gpu_snapshot)\n",
    "    \n",
    "            res = pd.read_csv(file, sep=\"\\s+\",header=None,names=['time','pass','fold','a','i','alpha','lambda','trainrmse','ivalidrmse','validrmse'])\n",
    "            \n",
    "            res['rel_acc'] = ((38000- res['validrmse'])/(38000-28000))\n",
    "            res['alpha_prime'] = res['alpha'] + res['fold'].apply(lambda x: new_alpha(x))\n",
    "\n",
    "            best = res.ix[res['rel_acc']==np.max(res['rel_acc']),:]\n",
    "            plot_glm_results(ax3, res, best.tail(1), cb)\n",
    "            # flag for colorbar to avoid redrawing\n",
    "            cb = True\n",
    "\n",
    "            # Add footnotes\n",
    "            footnote_text = \"*U.S. Census dataset (predict Income): 45k rows, 10k cols\\nParameters: 5-fold cross-validation, \" + r'$\\alpha = \\{\\frac{i}{7},i=0\\ldots7\\}$' + \", \"\\\n",
    "   'full $\\lambda$-' + \"search\"\n",
    "            #pl.figtext(.05, -.04, footnote_text, fontsize = 14,)\n",
    "            pl.annotate(footnote_text, (0,0), (-30, -50), fontsize = 12,\n",
    "                        xycoords='axes fraction', textcoords='offset points', va='top')\n",
    "\n",
    "            #update the graphics\n",
    "            display.display(pl.gcf())\n",
    "            display.clear_output(wait=True)\n",
    "            time.sleep(0.01)\n",
    "\n",
    "            #save the images\n",
    "            saveimage=0\n",
    "            if saveimage:\n",
    "                file_name = './images/glm_run_%04d.png' % (i,)\n",
    "                pl.savefig(file_name, dpi=200)\n",
    "            i=i+1\n",
    "        \n",
    "        except KeyboardInterrupt:\n",
    "            break\n",
    "        #except:\n",
    "        #    #print(\"Could not Create Frame\")\n",
    "        #    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose Data Size and Order and How was processed for H2OAIGLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n=8550 mTrain=4454 mValid=1114\n",
      "fortran=1\n",
      "{dtype('float64')}\n",
      "1\n",
      "double precision\n"
     ]
    }
   ],
   "source": [
    "intercept = 1 #\n",
    "standardize = 0\n",
    "n=train_data_mat.shape[1]\n",
    "mTrain=train_data_mat.shape[0]\n",
    "if FRACTION<1.0:\n",
    "    mValid=test_data_mat.shape[0]\n",
    "else:\n",
    "    mValid=0\n",
    "print(\"n=%d mTrain=%d mValid=%d\" % (n,mTrain,mValid))\n",
    "# Order of data\n",
    "fortran = 1\n",
    "print(\"fortran=%d\" % (fortran))\n",
    "result={df[k].dtype for k in df.columns}\n",
    "print(result)\n",
    "print(fortran)\n",
    "if result.pop() == np.dtype('float64'):\n",
    "    print(\"double precision\")\n",
    "    precision=1\n",
    "else:\n",
    "    print(\"single precision\")\n",
    "    precision=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Help function to use Mapd->pygdf data pointers in GPU on H2OAIGLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def RunH2Oaiglm(arg):\n",
    "    intercept,standardize, lambda_min_ratio, nFolds, nAlphas, nLambdas, nGPUs = arg\n",
    "    \n",
    "        # set solver cpu/gpu according to input args\n",
    "    if((nGPUs>0) and (h2oaiglm.ElasticNetSolverGPU is None)):\n",
    "        print(\"\\nGPU solver unavailable, using CPU solver\\n\")\n",
    "        nGPUs=0\n",
    "\n",
    "    sharedA = 0\n",
    "    sourceme = 0\n",
    "    sourceDev = 0\n",
    "    nThreads = 1 if(nGPUs==0) else nGPUs # not required number of threads, but normal.  Bit more optimal to use 2 threads for CPU, but 1 thread per GPU is optimal.\n",
    "\n",
    "    #print(\"Setting up Solver\")\n",
    "    os.system(\"rm -f rmse.txt ; touch rmse.txt ; rm -f varimp.txt ; touch varimp.txt\")\n",
    "    Solver = h2oaiglm.ElasticNetSolverGPU if(nGPUs>0) else h2oaiglm.ElasticNetSolverCPU\n",
    "\n",
    "\n",
    "    #  Solver = h2oaiglm.ElasticNetSolverCPU\n",
    "    assert Solver != None, \"Couldn't instantiate ElasticNetSolver\"\n",
    "    enet = Solver(sharedA, nThreads, nGPUs, 'c' if fortran else 'r', intercept, standardize, lambda_min_ratio, nLambdas, nFolds, nAlphas)\n",
    "\n",
    "    # Not using weights\n",
    "    e=c_void_p(0)\n",
    "\n",
    "    print(\"Solving\")\n",
    "    ## Solve\n",
    "    t0 = time.time()\n",
    "    print(\"vars: %d %d %d %d %d %d %d\" % (sourceDev, mTrain, n, mValid, intercept, standardize, precision))\n",
    "    enet.fit(sourceDev, mTrain, n, mValid, intercept, standardize, precision, a, b, c, d, e)\n",
    "    t1 = time.time()\n",
    "    print(\"Done Solving\")\n",
    "    print(\"Time to train H2O AI GLM: %r\" % (t1-t0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train 4000 Elastic Net Models (5-fold cross-validation, 8 $\\alpha$ values, 100 $\\lambda$ values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lambda_min_ratio=1E-9\n",
    "nFolds=5\n",
    "nAlphas=8\n",
    "nLambdas=100\n",
    "nGPUs=maxNGPUS # choose all GPUs\n",
    "\n",
    "\n",
    "if run==1 and anim==0:\n",
    "    # Run Model\n",
    "    arg = intercept,standardize, lambda_min_ratio, nFolds, nAlphas, nLambdas, nGPUs \n",
    "    RunH2Oaiglm(arg)\n",
    "\n",
    "if run==1 and anim==1:\n",
    "    from threading import Thread\n",
    "\n",
    "    # Run Model\n",
    "    arg = intercept,standardize, lambda_min_ratio, nFolds, nAlphas, nLambdas, nGPUs \n",
    "    background_thread = Thread(target=RunH2Oaiglm, args=(arg,))\n",
    "    background_thread.start()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![ScreenShot](gtc-2017-anaconda_v3_key.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uISEBa9aswcCBA4VMp169eli8eHGuxxs8eDAcHR3h6OiIWbNm5fi8cePGQsE+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\nwj9NNqfOnvUCyGrOnZfs7wLQ3YIrERG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dLEq36CP+7XDVxZD/7qnLRv314fgpeSklJoCNfixYv1x/4KfNarV6/I4Ynu1yEsLIxXXnnF5/AF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cys89DyMSyf67snMHiWefxe/rxaypofzc87Drsv/m+P399D3+GG5LC0ZJnPiSpcQXnzD0O+GT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NAF2frC8zycLDzBu7hZuXnJ2u2QMbAAnTB0ROHQLjEXQqDlW0N6zt0RuoYRGD5BTkUZ7lLr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\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ye/ZsLrnkkgPOPeecc5g5cybf+c53Dvi667r867/+K57nccYZZ3DttdeOyLWY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\niBBCCCGEEEIIIaQO+X+Cf2MO6sMkwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f81c7731cf8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 599,
       "width": 577
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if run==1 and anim==1:\n",
    "    # Show Animation\n",
    "    arg = nGPUs\n",
    "    RunAnimation(arg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
